A Secure and Decentralized Trust Management Scheme for Smart Health Systems
A Secure and Decentralized Trust Management Scheme for Smart Health Systems
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DOI：
10.1109/jbhi.2021.3107339
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发表时间：
2022-05-01
影响因子：
7.7
通讯作者：
Tadayon, Mohammad Hesam
中科院分区：
文献类型：
--
作者：
Ebrahimi, Maryam;Haghighi, Mohammad Sayad;Tadayon, Mohammad Hesam
物联网（IoT）的增长非常快，现在它也已经找到了医疗保健应用的方式。许多智能健康小工具和设备正在帮助从业者收集医疗信息和监测患者。在这种分布式系统中，信息或服务有时被其他设备共享和使用。考虑到与健康有关的信息以及根据这些信息作出的决定的重要性，应当对所提供的服务或信息的安全和质量作出某种保证。在这些情况下，信任管理是提高应用程序安全性和可靠性的有效手段。然而，由于物联网特有的一些限制，传统的信任评估算法无法使用或无法产生令人满意的结果。本文利用证据理论设计了一种面向服务的分散式医疗物联网信任管理模型。证据距离的测量用于奖励行为良好的医疗保健服务/信息提供者以及恶意实体。在这个上下文感知模型中，信任是基于直接经验和间接反馈的访问者。该过程在两个上下文中运行：信任医疗服务和信任推荐。当个人的直接经验不存在，信任的来源或服务的估计，通过应用证据理论的组合规律，并整合间接的信任值。该模型由于其动态参数和使用证据距离的概念，可以抵抗恶意攻击、善意攻击和开-关攻击。我们的结果证实了该方案的鲁棒性和效率。
The Internet of Things (IoT) growth is extremely fast and it now has found its way to healthcare applications too. Many smart health gadgets and devices are helping practitioners in collecting medical information and monitoring patients. In this distributed system, information or service is sometimes shared and used by other devices. Considering the importance of health-related information and the decisions made based on it, there should be some sort of assurance on the security and quality of the services or information provided. Trust management is an efficient means of promoting application security and reliability in these cases. However, due to some limitations that are specific to IoT, traditional trust evaluation algorithms cannot be employed or do not yield satisfactory results. In this paper, evidence theory is exploited to design a decentralized service-oriented trust management model for healthcare IoT. A measure of evidence distance is used to reward well-behaving healthcare service/information providers as well as referrers and punish malicious entities. In this context-aware model, trust is estimated based on direct experiences and indirect feedbacks of recommenders. The process runs in two contexts; trust to healthcare service and trust to recommendation. When personal direct experience does not exist, trust to a source or service is estimated by applying the combinatorial laws of evidence theory and integrating indirect trust values. The proposed model is secure against bad-mouthing, good-mouthing, and on-off attacks due to its dynamic parameters and using the concept of evidence distance. Our results confirm the robustness and efficiency of this scheme.